OUR PARTNERS
HOW IT WORKS
Step 1
Image Acquisition
High-resolution images of products are captured using digital cameras or imaging devices.
Preprocessing
Image quality is enhanced through operations such as noise reduction, resizing, and color normalization.
Step 2
Step 3
Feature Extraction
Relevant features such as texture, color, shape, and intensity are extracted from the preprocessed images.
Defect Detection
Machine learning algorithms spot deviations from normal features, indicating possible defects.
Step 4
Step 5
Classification
Detected defects are classified into predefined categories using AI algorithms
Post-processing
Refinement techniques are used to remove false positives and ensure accurate results
Step 6
AI-Powered Labeling
AI-powered labeling speeds up the process by using artificial intelligence to quickly and accurately label images
FEATURES
Train DefectGaurd on your Dataset
Plug & Play
Facilitates simple "Plug n Play" integration for a seamless setup. Enjoy easy deployment with an intuitive interface.
No Expertise Required
Designed for ease of use, no technical expertise necessary. Intuitive interface for straightforward deployment of customized solutions.
Compatible with Edge Devices
Leveraging edge computing, data is processed locally near its source, reducing latency, boosting efficiency, and enhancing overall system performance.
Collaborative Platform
A powerful system enabling team members to seamlessly collaborate on labeling and training workloads for computer vision models in quality inspection, enhancing productivity and accuracy.
Fully Customizable
Offers robust customization, letting you tailor the labeling and training process to your specific needs, ensuring efficient and accurate computer vision model training for quality inspection.
DEFECTGUARD IN ACTION
INDUSTRY SOLUTIONS
Textile
Food & Beverages
Metal & Steel
Package Handling
Plastic Molding
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Wood
